Centralized analytics of multiple visual inspection appliances

ABSTRACT

A visual inspection data collection and analysis system comprising: a plurality of visual inspection appliances (VTA) configured to inspect and acquire visual inspection data relating to inspected items; and a data collection and analytics server (DCAS) configured to receive information comprising the visual inspection data from the multiple VIAs and to analyze the received information to form a big data analysis. The VIAs are adapted for detecting defects or gating or counting the inspected items without the involvement of the DCAS.

FIELD

The present invention relates to visual inspection of items on aproduction line and more specifically to collection and analysis of datafrom multiple visual production line inspection appliances.

BACKGROUND

Inspection during production processes helps control the quality ofproducts by identifying defects and then acting upon this detection, forexample, by fixing the defect or discarding the defective part. Theprocess of defect detection is essential for quality assurance (QA),gating, and sorting on production lines, and is consequently useful inimproving productivity, improving production processes and workingprocedures, reducing defect rates, and reducing re-work and waste.

Automated visual inspection methods are used in production lines toidentify visually detectable anomalies that may have a functional oraesthetic impact on a manufactured part. Due to the underlyingtechnologies that drive them, current visual inspection solutions forproduction lines are: (1) typically highly customized to a particularproduct and the particular QA, gating, or sorting task that isaddressed; (2) very expensive; (3) very time consuming to set up; (4)require expert selection and integration of hardware, cameras, lightingand software components; and require expert maintenance of thesethroughout the lifetime of the inspection solution and the productionline.

As a result of the intensive costs, human resources, and sheer timerequirements, that are associated with tailoring inspection solutions asdescribed above, manufacturing plants are able to deploy only a limitednumber of such systems. Further, due to the tailored nature of thesolutions a single plant may use several inspection systems fromdifferent integrators. Gathering data from these disparate systems thusrequires a dedicated integration project that itself is tailored andinflexible to integrate the different data formats and protocols of eachsolution. Therefore, prior art solutions don't provide the capability toshare data from different parts of the plants or across multiple plantsand thus can't provide comprehensive analysis of the data gathered.

Also, due to the limited number of inspection solutions it is usuallynot necessary or helpful to centrally analyze the data from the variousinspection solutions and each solution stores any inspection datalocally. Not correlating the results from the visual inspection systemsthat are installed in a plant can result in decreased quality resultingin potential loss of reputation and even financial claims against theplant for inferior products.

SUMMARY

The present invention overcomes the drawbacks of the prior art bydeploying simplified visual inspection systems that enable gathering ofinspection data and enable to determine trends in the production plant.Embodiments of the invention provide multiple automated visualinspection appliances (VIA) for a production plant and a centralizeddata collection and analytics server (DCAS) that gathers and analyzesdata from the VIAs. The DCAS can then provide reports, dashboards andalerts to determine production trends in the manufacturing plant andthus improve the quality and productivity of the plant.

Each VIA can be easily and quickly installed for inspection withoutsignificant tailored integration. The ease of setup and operation isenabled by a combination of machine learning, and computer visionalgorithms that dynamically adapt to assess the item to be inspected,the target area of inspection, and the characteristics of thesurrounding environment effecting the inspection setup.

Each VIA comprises a flexible mounting assembly, a camera assembly whichcomprises an inspection camera and lighting source, and a controllerwherein the inspection camera and lighting source are both connected toand controlled by the controller. The VIAs are in wireless or wiredcommunication with the DCAS.

Once the mount and camera assemblies are installed—a process that doesnot require skilled staff—the VIA can be initiated. In use, defect freeembodiments of items to be inspected are first processed in a setupstage where the controller learns parameters of the items as captured inimages by the camera assembly. In some embodiments no database ofdefects is used and only defect-free items are analyzed during the setupstage. Items to be inspected preferably comprise any item type, shape ormaterial, set in any lighting environment.

In the inspection stage, inspected items, (manufactured items that areto be inspected for inspection tasks, such as, defect detection, gatingor sorting purposes), are imaged and the image data collected by thecamera from each inspected item is processed by the controller. Thecontroller uses machine learning algorithms which may providehuman-level analysis of defects in inspection images preferably evenwith differing illumination conditions, different reflections, shading,varying location, shape tolerances, etc. This inspection data collectedfrom the VIAs is sent to the DCAS for analysis.

According to some embodiments of the present invention a visualinspection data collection and analysis system, comprises: a pluralityof visual inspection appliances (VIA) configured to inspect and acquirevisual inspection data relating to inspected items; and a datacollection and analytics server (DCAS) configured to receive informationcomprising the visual inspection data from the multiple VIAs and toanalyze the received information to form big data analysis. Preferablythe VIAs are adapted for detecting defects or gating or counting theinspected items without the involvement of the DCAS.

Optionally the inspected items are different types of items. Optionallythe big data analysis comprises a combination of information related todifferent types of inspection items. Optionally the DCAS furthercomprises a display and wherein the DCAS outputs the analysis to thedisplay. Optionally the acquired inspection data from each one of themultiple VIAs is selected from the group consisting of: image/s of theinspected item; record of decision by VIA whether an item has a defect;images of the defects; number of defects; records of deviations fromgood item samples which are not significant enough to be reported asdefects but can imply to issues in the production line; item unique ID;plant work/job order; batch ID; personnel in charge of the productionline or station; production tool ID; part name; part serial number;production tool ID; and a combination of the above.

Optionally the data is communicated from a VIA to the DCAS according totiming selected from the group consisting of: after inspection of eachitem by each VIA; after inspection of a configurable number of items perVIA; after a configurable period of time per VIA; based on a dateschedule; based on a time of day schedule; and a combination of theabove.

Optionally the analysis is selected from the group consisting of: rootcause analysis of detected defects; predictive maintenanceanalysis—based on detecting trends in defect or deviations that are notdefects; intensity of the defects—analysis of trends to increasingoccurrences of defects per period of time; significance of thedefects—analysis of trends of increasing effect of defects or deviationson the produced item; analysis of product deviations from ideal that arenot defects but indicate a trend towards decreasing quality; analysis ofdefect shape, area and type of defect optionally in the form of a defect“map”; cost of defect—i.e. the cost of discarded items or cost of repairof items determined to be defective; product recall and/or latentproduct fault vs. defect and/or product deviation history analysis;supplier analysis comparing product raw material suppliers vs defects;and relationship analysis between different production stages of thesame item.

Optionally the DCAS is adapted to issue reports based on receivedinspection data wherein the reports are selected from the groupconsisting of: % defects detected per item; defect report includingimages of item showing where defects were detected; % defects detectedper manufacturing area; number of items inspected per period of time;personnel vs item defect report; % defects per shift; % defects permanufacturing type (e.g. casting lines vs molding lines); % defects perdefect type; defect report per period of time and production area; and acombination of the above.

Optionally the DCAS is adapted to initiate activity on one or more VIAs,the activity selected from the group consisting of: DCAS checks theoperational status of one or more VIAs; DCAS checks the software versionrunning on one or more VIAs; DCAS checks the security update status ofone or more VIAs; DCAS accesses a real-time view of the inspectionimages from one or more VIAs; DCAS requests specific data from one ormore VIAs; DCAS changes inspection or other settings of one or moreVIAs; DCAS performs software upgrades to one or more VIAs; DCASinitiates inspection to be performed by one or more VIAs; DCAS changesthe region of interest to be inspected by one or more VIAs; DCASinitiates re-inspection of previously inspected items; DCAS initiatesre-inspection of previously inspected items with changed inspectionparameters; and a combination of the above.

Optionally the DCAS is adapted to store the received visual inspectiondata. Optionally the stored inspection data can be searched. Optionally,the DCAS is adapted for issuing alerts based on the analysis. Optionallythe DCAS is adapted to run 3^(rd) party applications adapted to produceanalyses and reports based on the stored inspection data.

As used herein the term “item” refers to a production item whereinproduction items may be different production stages of the same productor may be different products or different production stages of differentproducts or the same item inspected from different angles. Items may beof any type, shape, size, material, or any other attribute and noexample herein should be considered limiting.

As used herein, the term “defect” may include, for example, a visibleflaw on the surface of an item, an undesirable size, shape or color ofthe item or of parts of the item, an undesirable number of parts of theitem, a wrong or missing assembly of its interfaces, a broken or burnedpart, an incorrect alignment of an item or parts of an item, and ingeneral, any difference between a defect free sample and the inspecteditem. Optionally or additionally a defect is a difference which would beevident to a human user between a defect free item (and/or group ofdefect free items) and a same-type inspected item. In some embodiments adefect may include flaws which are visible only in enlarged or highresolution images, e.g., images obtained by microscopes or otherspecialized cameras.

Inspection of items as described herein should also be understood asinspection for purposes of defect detection, gating and/or sorting.Where one of these terms is used e.g.: “defect detection” this should beunderstood as referring to any one of inspection tasks, such as, defectdetection, gating, or sorting.

A plant as used herein refers to a manufacturing environment whichcontains one or more production lines or production areas formanufacture, assembly, testing, packaging or any other type ofindustrial processing of items.

The processes described below refer, for simplicity, to “images”,however it should be appreciated that the processes described herein maybe carried out on image data other than or in addition to full images.The term “images” also includes video captured by the cameras of thepresently described system.

The term “product stage” as used herein should be understood to includeany of an assembly stage (items are assembled into a product),manufacturing stage (items are subjected to a form of processing as partof product manufacture), and/or inspection stage (stages are actuallydifferent views or sections of the same product). As used herein productstages are related to one another by their being production stages oraspects of a product. The term item may be used to refer to a productstage. As used herein a “product” may refer to a completed commercialproduct but may also refer to a manufactured item or part that isdestined for integration into a product.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. The materials, methods, andexamples provided herein are illustrative only and not intended to belimiting.

Implementation of the method and system of the present inventioninvolves performing or completing certain selected tasks or stepsmanually, automatically, or a combination thereof. Moreover, accordingto actual instrumentation and equipment of preferred embodiments of themethod and system of the present invention, several selected steps couldbe implemented by hardware or by software on any operating system of anyfirmware or a combination thereof. For example, as hardware, selectedsteps of the invention could be implemented as a chip or a circuit. Assoftware, selected steps of the invention could be implemented as aplurality of software instructions being executed by a computer usingany suitable operating system. In any case, selected steps of the methodand system of the invention could be described as being performed by adata processor, such as a computing platform for executing a pluralityof instructions.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “analyzing”, “processing,”“computing,” “calculating,” “determining,” “detecting”, “identifying” orthe like, refer to the action and/or processes of a computer, or similarelectronic computing device as defined below, that manipulates and/ortransforms data represented as physical, such as electronic, quantitieswithin the computing system's registers and/or memories into other datasimilarly represented as physical quantities within the computingsystem's memories, registers or other such information storage,transmission or display devices. Unless otherwise stated, these termsrefer to automatic action of a processor, independent of and without anyactions of a human operator.

As used herein the terms “machine learning” or “artificial intelligence”refer to use of algorithms on a computing device that parse data, learnfrom this data, and then make a determination, where the determinationis not deterministically replicable (such as with deterministicallyoriented software as known in the art).

Although the present invention is described with regard to a “computingdevice”, a “computer”, or “mobile device”, it should be noted thatoptionally any device featuring a data processor and the ability toexecute one or more instructions may be described as a computer,including but not limited to any type of personal computer (PC), aserver, a distributed server, a virtual server, a cloud computingplatform, a cellular telephone, an IP telephone, a smartphone, or a PDA(personal digital assistant). Any two or more of such devices incommunication with each other may optionally comprise a “network” or a“computer network”.

BRIEF DESCRIPTION OF THE FIGURES

The invention will now be described in relation to certain examples andembodiments with reference to the following illustrative figures so thatit may be more fully understood. In the drawings:

FIGS. 1A-1B are illustrative schematic drawings showing collection ofdata from automated visual inspection appliances on a production lineaccording to at least some embodiments of the present invention; and

FIG. 2 is a flow diagram showing a process for collection of data fromautomated visual inspection appliances on a production line according toat least some embodiments of the present invention.

DETAILED DESCRIPTION

The present invention in at least some embodiments is for a systemcomprising multiple automated visual inspection appliances (VIA) for aproduction plant and a centralized data collection and analytics server(DCAS) that gathers and analyzes data from the VIAs. Reference is nowmade to FIGS. 1A-1B which are illustrative schematic drawings showingcollection of data from automated visual inspection appliances on aproduction line according to at least some embodiments of the presentinvention. As shown in FIG. 1A an automated visual inspection system 100comprises multiple visual inspection appliances (VIA) 110A, B,C, n incommunication with a data collection and analytics server (DCAS) 150.Although four VIAs 110A, 110B, 110C and 110 n are shown, it should beappreciated that any number of VIAs may be in communication with DCAS150. VIA is preferably provided as an integrated appliance for use in amanufacturing environment or plant. VIAs 110A, B,C, n are optionallyinstalled in one plant or optionally multiple VIAs are installed inmultiple plants. Each VIA connects to DCAS 150 using wired or wirelesscommunications protocols and methods as known in the art.

DCAS 150 is a computing device as defined above and may optionallycomprise a server, distributed server, cloud computing environment, datacluster or any other suitable computing device. DCAS 150 preferablycomprises analysis engine 152, database (DB) 154, DCAS user interface(UI) 156, and notification engine 158.

Analysis engine 152 receives data from VIAs 110A, B, C and n andanalyses the received data to output insights, recommendations,summaries, trends, alerts, and root cause analysis of defects allrelated to the items inspected and the production environment asdescribed below. Analysis engine 152 optionally uses big data analysismethods.

DB 154 is a database (e.g., as known in the art) and stores datatransmitted by VIAs 110A, B, C and n and also results and interimresults of analysis by engine 152. DB 154 also stores configuration datadefined in DCAS 150 for system 100 including VIA profiles. A VIA profileincludes information about each VIA in system 100 including but notlimited to: unique identifier, name, physical mounting details, positionin plant, plant geolocation, items inspected, reference images of itemsinspected, profiles of items inspected, inspection results and so forth.Optionally a manufacturing area 170 is defined for DCAS 150 where eachmanufacturing area 170 includes one of more VIAs. A manufacturing area170 optionally comprises VIAs from one plant or optionally comprisesVIAs from multiple plants. The manufacturing area 170 defined in FIG. 1Aincludes VIAs 110B and 110C but it should be appreciated that any numberor any of VIAs could be included in a manufacturing area 170 and anynumber of manufacturing areas 170 may be defined for DCAS 150. Wheremore than one manufacturing area 170 is defined these manufacturingareas 170 may optionally overlap, i.e.: a single VIA may optionally bepart of several manufacturing areas 170.

DCAS UI 156 enables display of the results of analysis engine 152 andalso interaction with DCAS 150 by a human operator (not shown). DCAS UI156 optionally comprises a monitor or screen and information provided toa user of DCAS 150 may be visual (e.g., text or other content displayedon the monitor). Alternatively or additionally, DCAS UI 156 comprises anaudio player to emit a sound. DCAS UI 156 preferably enables acceptinguser input such as by a touch screen, keyboard and/or mouse. Optionally,DCAS UI 156 is provided on a multi-purpose device such as a smartphone,tablet or personal computer in communication with DCAS 150. OptionallyDCAS UI 156 can be accessed remotely optionally from within the plantwhere it operates and outside of the plant where it operates.

Notification engine 158 is in communication with external communicationnetworks 70 and provides push notification of alerts or other outputsfrom analysis engine 152. Non-limiting types of notification methodsinclude email, SMS, WhatsApp or any mobile notification mechanism.Notification engine 158 can be configured via DCAS UI 156 to definerecipients and notification methods for different types of alerts,reports or analyses.

In some embodiments, VIAs 110A, B, C, n and DCAS 150 communicate overthe external network 70. DCAS 150 may automatically detect when a VIA isconnected to the external network 70 and may then register the newlyconnected VIA and perform data collection and analysis of the VIAperformance and of data obtained by the VIA, as described herein.

Thus, in one embodiment, a visual inspection data collection andanalysis system, includes a plurality of VIAs configured to acquirevisual inspection data relating to inspected items, and a centralserver, such as a DCAS configured to identify a newly connected VIA on acommunications network, to register the newly connected VIA and enabledata collection and analysis of each registered VIA.

The DCAS 150 may identify each VIA based on an ID, IP address or otherunique identifiers connected to each VIA and each VIA may be registeredunder a unique identifier. Data collection and analysis of eachregistered VIA may be done according to the registered uniqueidentifier. E.g., data from VIAs registered under an identifier relatedto inspection line A may be analyzed differently from data from VIAsregistered under an identifier related to inspection line B.

DCAS 150 may detect when a VIA is connected to the external network 70based on signals sent over the network (e.g., ethernet) by DCAS 150and/or VIAs 110A, B, C and n. Signals may include, for example, packetstransmitted by multicast addressing using, for example User DatagramProtocol (UDP). Based on the signals, which may be transmittedperiodically by the DCAS and/or VIA, the DCAS can determine that a VIAis connected to the network and the DCAS may then compare the VIAidentifier to already registered VIA identifiers to determine if the VIAis newly connected or not.

DCAS 150 may perform one or more different actions for each registeredVIA, as described herein. For example, collecting visual inspection dataand the timing of the collection of data may be done based on theregistered VIA identifier. Storing the received data, analysis of thereceived data and issuing reports may be controlled based on theregistered VIA identifier. The DCAs may initiate different activities ineach VIA based on the registration of each VIA.

DCAS 150 is optionally in communication with an external monitoringsystem 60. Monitoring system 60 is a computing device as describedabove. Monitoring system 60 is typically a production plant managementsystem such as for gathering and monitoring key performance indicatorsfor manufacturing efficiency. Monitoring system 60 is optionally aproduction resource management platform.

DCAS 150 optionally runs 3^(rd) party applications 159 where 3^(rd)party application 159 are operative to produce analyses and reportsbased on the collected data, which may be stored in DCAS 150. Optionallythe 3^(rd) party applications 159 can operate VIAs according to thecapabilities of DCAS 150.

As shown in FIG. 1B each VIA 110 comprises a controller 130, cameraassembly 111, and mounting assembly 108. Camera assembly 111 comprisescamera 102, and light source 106.

Camera 102 comprises a CCD or CMOS or other appropriate imaging chip.Camera 102 is a 2D camera or optionally a 3D camera. Optionally camera102 comprises the camera integrated into a mobile device such as asmartphone or tablet where the device is attached to mounting assembly108. Camera 102 optionally comprises a polarizing lens, tele-centriclens, narrow band, zoom lens, or other lens (not shown) placed over thelens of camera 102 or directly upon its imaging chip.

Light source 106 comprises LEDs or other known light source. Theintensity (brightness) of light source 106 can be adjusted. Optionally,the color of light source 106 can be adjusted. Optionally, light source106 comprises multiple controllable segments, each of which can beactivated or provided with the same or different intensity and/or color.For example, but without intention to be limiting, light source 106 maycomprise a circular array of LEDs surrounding camera 102 lens, whereradial portions of circular light source 106 are controlled individuallyor alternatively the intensity and/or color of every LED or groupings ofLEDs, can be controlled individually.

Light source 106 is shown as positioned above camera 102 for simplicityof the figures but this position should not be considered limiting.Optionally, light source 106 is mounted on the side of or below camera102. Light source 106 is preferably attached to and surrounds or isotherwise fixed in relation to the lens of camera 102 so as toilluminate the field of view (FOV) 104 of camera 102 or portionsthereof. Camera assembly 111 is attached to mounting assembly 108.Alternatively, camera 102 and light source 106 are separately attachedto mounting assembly 108 allowing individual adjustment of the spatialposition of either.

Mounting assembly 108 comprises mounts, segments and fasteners allowingadaptation and adjustment of mounting assembly 108 for optimalpositioning of camera 102 and light source 106 for inspection of anitem.

Camera assembly 111 is positioned using mounting assembly 108 such thatitems 20 to be inspected are within the field of view 104 of camera 102.Mounting assembly 108 is attached to a mounting surface 40. Surface 40may remain in a fixed position relative to item 20 or alternatively maymove so as to repeatedly bring camera assembly 111 into a position whereitems 20 to be inspected are within the field of view 104 of camera 102.A non-limiting example of a moving surface 40 is a robot arm. Wherereference is made to FOV 104 herein it is to be understood that lightsource 106 is positioned to illuminate FOV 104. Surface 40 optionallycomprises an aluminum profile including grooves for attachment ofmounting brackets.

Items 20 to be inspected may be placed on an inspection line 30 whichcomprises means for supporting and moving items 20 such as but notlimited to a conveyor belt, or a cradle or another holding apparatus,moving in direction 22, such that first item 20 is brought into FOV 104followed by second item 20 which is brought into FOV 104, and so forth.Alternatively, items 20 are successively placed in FOV 104 and thenremoved such as by a robot or human operator.

Camera 102 and light source 106 are in communication with controller130. Controller 130 is a computing device as defined herein. Controller130 comprises one or more processors (not shown) such as but not limitedto a central processing unit (CPU), a graphics processing unit (GPU), adigital signal processor (DSP), a microprocessor, a controller, a chip,a microchip, an integrated circuit (IC), or any other suitablemulti-purpose or specific processor or controller. Controller 130activates light source 106 or any of its components or controllablesegments as described above, which may or may not be activated dependingon the item being imaged or the inspection lighting environment.Controller 130 preferably alters the intensity or color of light source106 depending on the item being imaged or the inspection lightingenvironment. Controller 130 preferably alters the intensity or color oflight source for regions of particular interest within the illuminatedarea.

Controller 130 further comprises a memory unit (not shown) which storesexecutable instructions that, when executed by the processor, facilitateperformance of operations of the processor. The memory unit may alsostore at least part of the image data received from camera 102.Non-limiting examples of memory units include random access memory(RAM), dynamic RAM (DRAM), flash memory, volatile memory, non-volatilememory, cache memory, a buffer, a short term memory unit, a long termmemory unit, or other suitable memory units or storage units.

Controller 130 further comprises a VIA user interface (UI) 132. VIA UI132 may comprise a monitor or screen and notifications to a user may bevisual (e.g., text or other content displayed on the monitor).Alternatively or additionally, VIA UI 132 comprises a light that maylight up or change color. Alternatively or additionally, VIA UI 132comprises an audio player to emit a sound. VIA UI 132 preferably enablesaccepting user input such as by a touch screen, keyboard and/or mouse.Optionally, VIA UI 132 is provided on a multi-purpose device such as asmartphone, tablet or personal computer.

Optionally DCAS 150 can check the operation status of one or more VIAs110. Optionally DCAS 150 can check the software version running on oneor more VIAs 110. Optionally DCAS 150 can check the security status ofVIAs (e.g., that one or more VIAs 110 are updated with the most recentsecurity updates). Optionally an operator can use DCAS 150 to access areal-time view of the inspection images from any VIA 110 for display onDCAS UI 156. Optionally an operator can use DCAS 150 to request specificdata from any one or more of VIAs 110. Optionally DCAS 150 can changeinspection or other settings of any one or more of VIA 110. OptionallyDCAS 150 can perform software upgrades of any one or more of VIA 110.Optionally DCAS 150 can initiate inspection to be performed by any oneor more of VIA 110. Optionally, DCAS 150 can change the region ofinterest to be inspected by any one or more of VIA 110. Optionally DCAS150 can initiate re-inspection of previously inspected items furtheroptionally with changed inspection parameters.

Reference is now made to FIG. 2 which is a flow diagram showingcollection of data from automated visual inspection appliances on aproduction line according to at least some embodiments of the presentinvention. Use of automated visual inspection system 100 preferablyproceeds according to process 200 as shown in FIG. 2. Before process 200can begin, VIA 110 is set up to enable inspection of items 20. System100 requires setup for each type of item or stage of item that is to beinspected. In the setup step, preferably at least two or more defectfree samples of a manufactured item 20 of the same type are placed insuccession within field of view 104 of camera 102. Each defect freesample of item 20 is imaged by camera 102. These images, which may bereferred to as setup images, are optionally obtained by using differentimaging parameters of camera 102 and lighting parameters of light source106. The images comprise image data such as pixel values that representthe intensity of reflected light as well partial or full images orvideos.

The setup images are analyzed by controller 130 using machinelearning/artificial intelligence (AI) and computer vision algorithms tocreate a complete representation of item 20 used for defect detection,gating, sorting and/or other inspection tasks, on the production line.Following the setup step and based on the information collected from thesample, defect-free items, the inspection process can begin andcontroller 130 can preferably detect and inspect further items of thesame type even if these further items were never previously presented,and determine whether these are defect-free. It should be noted that theinspection of items 20 for defect detection (determination of whetherthe item 20 is defect free or has a defect) or gating or sorting orcounting can be performed by VIA 110 independently of DCAS 150.

In step 202, items 20 are inspected by each VIA 110 for defectdetection, gating, or sorting purposes. The following data is collectedby each VIA 110 per item 20 as a result of the inspection process. Thisdata is herein referred to as “per-item collected data”, and one or moreof per-item collected data is referred to as “collected data”:

-   -   Image/s of the inspected item;    -   Record of decision by VIA whether item has a defect;    -   Image of the defects;    -   Number of defects;    -   Records of deviations from good item samples which are not        significant enough to be reported as defects but can imply to        issues in the production line;    -   Item unique ID;    -   Plant Work/Job order/Batch ID;    -   Personnel in charge of the production line or station;    -   Production tool ID (Die or molder, etc.).    -   Part Name    -   Part Serial Number    -   Inspection Profile ID

In step 204 the collected data is transmitted by each VIA 110 to DCAS150. As above the communication between VIA 110 and DCAS 150 may usestandard communication infrastructure and protocols as known in the art.Collected data is stored in DB 154. Collected data is transmitted by VIA110 to DCAS 150 according to one or more of the following:

-   -   After inspection of each item by each VIA;    -   After inspection of a configurable number of items per VIA;    -   After a configurable period of time per VIA;    -   Based on Date/Time schedule;    -   Based on a combination of the above.

Collected data stored in DB 154 can preferably be searched and queriedvia DCAS UI 156, DB 154 of DCAS 150 functioning as an archive. A nonlimiting example of such a query is a search for images and otherinspection data related to a specific item indexed by an item identifiersuch as but not limited to the item barcode or serial number.

In step 206 the collected data from VIAs 110 is analyzed by analysisengine 152 and/or used for generating reports. The analyses or use ofcollected data of step 206 optionally take place immediately followingstep 204. Alternatively, step 206 takes place some time after step 204.Reports and/or analyses are preferably generated using big data methods.Optionally the analysis is performed for a combination of different typeitems where different type items may be any of different products,different production stages, different plants, or different industries.One or more of the following reports are preferably generated includingbut not limited to:

-   -   % defects detected per item;    -   Defect report including images of item showing where defects        were detected;    -   % defects detected per manufacturing area;    -   Number of items inspected per period of time;    -   Personnel vs item defect report;    -   % defects per shift;    -   % defects per manufacturing type (e.g. casting lines vs molding        lines);    -   % defects per defect type;    -   Defect report per period of time and production area;        One or more of the following analyses are preferably performed        including but not limited to:    -   Root cause analysis of detected defects;    -   Predictive Maintenance analysis—based on detecting trends in        defect or deviations that are not defects;    -   Intensity of the defects—analysis of trends to increasing        occurrences of defects per period of time;    -   Significance of the defects    -   analysis of trends of increasing effect of defects or deviations        on the produced item;    -   Analysis of product deviations from ideal that are not defects        but indicate a trend towards decreasing quality;    -   Analysis of defect shape, area and type of defect optionally in        the form of a defect “map”;    -   Cost of defect—i.e. the cost of discarded items or cost of        repair of items determined to be defective;    -   Product recall and/or latent product fault vs. defect and/or        product deviation history analysis;    -   Supplier analysis comparing product raw material suppliers vs        defects;    -   Relationship analysis between different production stages of the        same item.

The analysis or reporting of step 206 preferably takes place based onone or more of:

-   -   Periodic for set periods of time which are preferably configured        independently per analysis;    -   Operator initiated where an operator of DCAS 150 defines and        initiates a specific analysis or report;    -   Analysis and/or reporting per number of items inspected        performed after a specific number of items have been inspected        by a specific VIA 110 or specific manufacturing area 170.

In step 208 the results of the analyses and/or reports of step 206 arestored in DB 154 and also preferably displayed using DCAS UI 156.Optionally, results are exported to external systems such as but notlimited to external monitor 60. Optionally results are displayed on aconfigurable dashboard presented on DCAS UI 156. Optionally results ofstep 206 generate alerts which are displayed on DCAS UI 156 orcommunicated to an operator of DCAS 150 such as via notification engine158 sending, for example but not limited to, text or other messages to amobile device. A non-limiting example of an alert is “% defects detectedin a production area exceeding a defined threshold”.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable sub-combination.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

1-17. (canceled)
 18. A visual inspection data collection and analysissystem, comprising: a. a plurality of visual inspection appliances (VIA)configured to acquire visual inspection data relating to inspecteditems; and b. a data collection and analytics server (DCAS) incommunication with the plurality of VIAs over a communications networkand configured to automatically detect and register each VIA newlyconnected to the network.
 19. The system of claim 18 wherein the DCAS isconfigured to register each VIA newly connected to the network under aunique identifier and initiate an activity in each VIA based on theidentifier of each VIA.
 20. The system of claim 19 wherein the activitycomprises one or a combination of: collecting visual inspection data,timing collection of the visual inspection data, storing received data,analysis of the received data and issuing reports.
 21. The system ofclaim 19 wherein the activity is selected from the group consisting ofone or a combination of: a. DCAS checks operational status of the VIA;b. DCAS checks software version running on the VIA; c. DCAS checkssecurity status of the VIA; d. DCAS accesses a real-time view of aninspected item from the VIA; e. DCAS requests specific data from theVIA; f. DCAS changes inspection or other settings of the VIA; g. DCASperforms software upgrades to the VIA; h. DCAS initiates inspection tobe performed by the VIA; i. DCAS changes a region of interest to beinspected by the VIA; j. DCAS initiates re-inspection of previouslyinspected items; and k. DCAS initiates re-inspection of previouslyinspected items with changed inspection parameters.
 22. The system ofclaim 18 wherein the DCAS is configured to receive the visual inspectiondata from the plurality of VIAs and to analyze said received informationto form a big data analysis.
 23. The system of claim 22 wherein theinspected items are different types of items.
 24. The system of claim 23wherein the big data analysis comprises a combination of informationrelated to the different types of items.
 25. The system of claim 22further comprising a display and wherein said DCAS outputs said analysisto said display.
 26. The system of claim 22 wherein said analysis isselected from the group consisting of: a. root cause analysis ofdetected defects; b. predictive maintenance analysis; c. intensity ofdefects; d. significance of defects; e. analysis of product deviationsfrom ideal that are not defects but indicate a trend towards decreasingquality; f. analysis of defect shape, area and type of defect; g. costof defect; h. product recall, latent product fault vs. defect or productdeviation history analysis; i. supplier analysis comparing product rawmaterial suppliers vs defects; and j. relationship analysis betweendifferent production stages of a same item.
 27. The system of claim 22wherein said DCAS is adapted to issue reports based on receivedinspection data wherein said reports are selected from the groupconsisting of: a. % defects detected per item; b. defect reportincluding images of item showing where defects were detected; c. %defects detected per manufacturing area; d. number of items inspectedper period of time; e. personnel vs item defect report; f. % defects pershift; g. % defects per manufacturing type; h. % defects per defecttype; i. defect report per period of time and production area; and j. acombination of the above.
 28. The system of claim 22 wherein said DCASis adapted to store said received visual inspection data and wherein thestored inspection data can be searched.
 29. The system of claim 28wherein said DCAS is adapted to run 3rd party applications adapted toproduce analyses and reports based on said stored inspection data. 30.The system of claim 22 wherein said DCAS is adapted for issuing alertsbased on said analysis.
 31. The system of claim 18 wherein the visualinspection data from each one of the plurality of VIAs is selected fromthe group consisting of: a. image/s of an inspected item; b. record ofdecision by VIA whether an item has a defect; c. image of the defects;d. number of defects; e. records of deviations from good item sampleswhich are not significant enough to be reported as defects but can implyissues in a production line; f. item unique ID; g. plant work/job order;h. batch ID; i. personnel in charge of a production line or station; j.production tool ID; k. part name; l. part serial number; m. productiontool ID; and n. a combination of the above.
 32. The system of claim 18wherein said visual inspection data is communicated from a VIA to saidDCAS according to timing selected from the group consisting of: a. afterinspection of each item by each VIA; b. after inspection of aconfigurable number of items per VIA; c. after a configurable period oftime per VIA; d. based on a date schedule; e. based on a time of dayschedule; and f. a combination of the above.